



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCamelCase__      =			logging.get_logger(__name__)

def 	lowerCAmelCase__    (				_SCREAMING_SNAKE_CASE							:      str     ):
				"""simple docstring"""







				if isinstance(_SCREAMING_SNAKE_CASE					,  (list, tuple)     ) and isinstance(videos[0]					,  (list, tuple)     ) and is_valid_image(videos[0][0]     ):
								return videos

				elif isinstance(_SCREAMING_SNAKE_CASE					,  (list, tuple)     ) and is_valid_image(videos[0]     ):
								return [videos]

				elif is_valid_image(_SCREAMING_SNAKE_CASE     ):
								return [[videos]]

				raise ValueError(f"Could not make batched video from {videos}"     )






class  SCREAMING_SNAKE_CASE      (				snake_case_       ):
							__lowerCamelCase						:						Dict     =['pixel_values']





							def __init__( self		:		str   ,					__lowercase		:		bool = True   ,					__lowercase		:		Dict[str, int] = None   ,					__lowercase		:		PILImageResampling = PILImageResampling.BILINEAR   ,					__lowercase		:		bool = True   ,					__lowercase		:		Dict[str, int] = None   ,					__lowercase		:		bool = True   ,					__lowercase		:		Union[int, float] = 1 / 255   ,					__lowercase		:		bool = True   ,					__lowercase		:		Optional[Union[float, List[float]]] = None   ,					__lowercase		:		Optional[Union[float, List[float]]] = None   ,					**__lowercase		:		List[Any]   ,					):

											'''simple docstring'''
											super().__init__(**lowerCAmelCase__						)
											__a	  =		size if size is not None else {'''shortest_edge''': 224}
											__a	  =		get_size_dict(lowerCAmelCase__   ,					default_to_square=lowerCAmelCase__						)
											__a	  =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__a	  =		get_size_dict(lowerCAmelCase__   ,					param_name="""crop_size"""						)

											__a	  =		do_resize
											__a	  =		size
											__a	  =		do_center_crop
											__a	  =		crop_size
											__a	  =		resample
											__a	  =		do_rescale
											__a	  =		rescale_factor
											__a	  =		do_normalize
											__a	  =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__a	  =		image_std if image_std is not None else IMAGENET_STANDARD_STD





							def     UpperCamelCase_       ( self		:		Dict   ,					__lowercase		:		np.ndarray   ,					__lowercase		:		Dict[str, int]   ,					__lowercase		:		PILImageResampling = PILImageResampling.BILINEAR   ,					__lowercase		:		Optional[Union[str, ChannelDimension]] = None   ,					**__lowercase		:		List[Any]   ,					):

											'''simple docstring'''
											__a	  =		get_size_dict(lowerCAmelCase__   ,					default_to_square=lowerCAmelCase__						)
											if "shortest_edge" in size:
															__a	  =		get_resize_output_image_size(lowerCAmelCase__   ,					size["""shortest_edge"""]   ,					default_to_square=lowerCAmelCase__						)
											elif "height" in size and "width" in size:
															__a	  =		(size['''height'''], size['''width'''])
											else:
															raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"						)
											return resize(lowerCAmelCase__   ,					size=lowerCAmelCase__   ,					resample=lowerCAmelCase__   ,					data_format=lowerCAmelCase__   ,					**lowerCAmelCase__						)





							def     UpperCamelCase_       ( self		:		int   ,					__lowercase		:		np.ndarray   ,					__lowercase		:		Dict[str, int]   ,					__lowercase		:		Optional[Union[str, ChannelDimension]] = None   ,					**__lowercase		:		Dict   ,					):

											'''simple docstring'''
											__a	  =		get_size_dict(lowerCAmelCase__						)
											if "height" not in size or "width" not in size:
															raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"						)
											return center_crop(lowerCAmelCase__   ,					size=(size["""height"""], size["""width"""])   ,					data_format=lowerCAmelCase__   ,					**lowerCAmelCase__						)





							def     UpperCamelCase_       ( self		:		List[str]   ,					__lowercase		:		np.ndarray   ,					__lowercase		:		Union[int, float]   ,					__lowercase		:		Optional[Union[str, ChannelDimension]] = None   ,					**__lowercase		:		int   ,					):

											'''simple docstring'''
											return rescale(lowerCAmelCase__   ,					scale=lowerCAmelCase__   ,					data_format=lowerCAmelCase__   ,					**lowerCAmelCase__						)





							def     UpperCamelCase_       ( self		:		Optional[Any]   ,					__lowercase		:		np.ndarray   ,					__lowercase		:		Union[float, List[float]]   ,					__lowercase		:		Union[float, List[float]]   ,					__lowercase		:		Optional[Union[str, ChannelDimension]] = None   ,					**__lowercase		:		Dict   ,					):

											'''simple docstring'''
											return normalize(lowerCAmelCase__   ,					mean=lowerCAmelCase__   ,					std=lowerCAmelCase__   ,					data_format=lowerCAmelCase__   ,					**lowerCAmelCase__						)





							def     UpperCamelCase_       ( self		:		List[Any]   ,					__lowercase		:		ImageInput   ,					__lowercase		:		bool = None   ,					__lowercase		:		Dict[str, int] = None   ,					__lowercase		:		PILImageResampling = None   ,					__lowercase		:		bool = None   ,					__lowercase		:		Dict[str, int] = None   ,					__lowercase		:		bool = None   ,					__lowercase		:		float = None   ,					__lowercase		:		bool = None   ,					__lowercase		:		Optional[Union[float, List[float]]] = None   ,					__lowercase		:		Optional[Union[float, List[float]]] = None   ,					__lowercase		:		Optional[ChannelDimension] = ChannelDimension.FIRST   ,					):

											'''simple docstring'''
											if do_resize and size is None or resample is None:
															raise ValueError("""Size and resample must be specified if do_resize is True."""						)

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""						)

											if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""						)

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""						)

											# All transformations expect numpy arrays.
											__a	  =		to_numpy_array(lowerCAmelCase__						)

											if do_resize:
															__a	  =		self.resize(image=lowerCAmelCase__   ,					size=lowerCAmelCase__   ,					resample=lowerCAmelCase__						)

											if do_center_crop:
															__a	  =		self.center_crop(lowerCAmelCase__   ,					size=lowerCAmelCase__						)

											if do_rescale:
															__a	  =		self.rescale(image=lowerCAmelCase__   ,					scale=lowerCAmelCase__						)

											if do_normalize:
															__a	  =		self.normalize(image=lowerCAmelCase__   ,					mean=lowerCAmelCase__   ,					std=lowerCAmelCase__						)

											__a	  =		to_channel_dimension_format(lowerCAmelCase__   ,					lowerCAmelCase__						)
											return image







							def     UpperCamelCase_       ( self		:		Tuple   ,					__lowercase		:		ImageInput   ,					__lowercase		:		bool = None   ,					__lowercase		:		Dict[str, int] = None   ,					__lowercase		:		PILImageResampling = None   ,					__lowercase		:		bool = None   ,					__lowercase		:		Dict[str, int] = None   ,					__lowercase		:		bool = None   ,					__lowercase		:		float = None   ,					__lowercase		:		bool = None   ,					__lowercase		:		Optional[Union[float, List[float]]] = None   ,					__lowercase		:		Optional[Union[float, List[float]]] = None   ,					__lowercase		:		Optional[Union[str, TensorType]] = None   ,					__lowercase		:		ChannelDimension = ChannelDimension.FIRST   ,					**__lowercase		:		Tuple   ,					):

											'''simple docstring'''
											__a	  =		do_resize if do_resize is not None else self.do_resize
											__a	  =		resample if resample is not None else self.resample
											__a	  =		do_center_crop if do_center_crop is not None else self.do_center_crop
											__a	  =		do_rescale if do_rescale is not None else self.do_rescale
											__a	  =		rescale_factor if rescale_factor is not None else self.rescale_factor
											__a	  =		do_normalize if do_normalize is not None else self.do_normalize
											__a	  =		image_mean if image_mean is not None else self.image_mean
											__a	  =		image_std if image_std is not None else self.image_std

											__a	  =		size if size is not None else self.size
											__a	  =		get_size_dict(lowerCAmelCase__   ,					default_to_square=lowerCAmelCase__						)
											__a	  =		crop_size if crop_size is not None else self.crop_size
											__a	  =		get_size_dict(lowerCAmelCase__   ,					param_name="""crop_size"""						)

											if not valid_images(lowerCAmelCase__						):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""						)

											__a	  =		make_batched(lowerCAmelCase__						)

											__a	  =		[
											    [
											        self._preprocess_image(
											            image=lowerCAmelCase__   ,					do_resize=lowerCAmelCase__   ,					size=lowerCAmelCase__   ,					resample=lowerCAmelCase__   ,					do_center_crop=lowerCAmelCase__   ,					crop_size=lowerCAmelCase__   ,					do_rescale=lowerCAmelCase__   ,					rescale_factor=lowerCAmelCase__   ,					do_normalize=lowerCAmelCase__   ,					image_mean=lowerCAmelCase__   ,					image_std=lowerCAmelCase__   ,					data_format=lowerCAmelCase__   ,					)
											        for img in video
											    ]
											    for video in videos
											]

											__a	  =		{'''pixel_values''': videos}
											return BatchFeature(data=lowerCAmelCase__   ,					tensor_type=lowerCAmelCase__						)





'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase_  :							Dict             =    {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_  :							List[Any]             =    [
							    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'EncodecModel',
							    'EncodecPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_encodec import (
							    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    EncodecConfig,
							)
							from .feature_extraction_encodec import EncodecFeatureExtractor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_encodec import (
														    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EncodecModel,
														    EncodecPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_  :							List[str]             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




